Experimental Study on GA-Based Path-Oriented Test Data Generation Using Branch Distance Function

Automatic path-oriented test data generation is not only a key problem but a hot issue in the research area of software testing today. Genetic algorithm (GA) has been used to path-oriented test data generation since 1992 and outperforms other approaches. A fitness function based on branch distance (BDBFF) has been applied in GA-based path-oriented test data generation. To investigate performance of this method, a triangle classification program was chosen as the benchmark. Using binary string coding, four combinations of selection and crossover operations were used to study performance of this method. Furthermore, the relationship between size of search space and average number of test data or average time was analyzed.